119 research outputs found
Constrained DMPs for Feasible Skill Learning on Humanoid Robots
In the context of humanoid skill learning, movement primitives have gained much attention because of their compact representation and convenient combination with a myriad of optimization approaches. Among them, a well-known scheme is to use Dynamic Movement Primitives (DMPs) with reinforcement learning (RL) algorithms. While various remarkable results have been reported, skill learning with physical constraints has not been sufficiently investigated. For example, when RL is employed to optimize the robot joint trajectories, the exploration noise could drive the resulting trajectory out of the joint limits. In this paper, we focus on robot skill learning characterized by joint limit avoidance, by introducing the novel Constrained Dynamic Movement Primitives (CDMPs). By controlling a set of transformed states (called exogenous states) instead of the original DMPs states, CDMPs are capable of maintaining the joint trajectories within the safety limits. We validate CDMPs on the humanoid robot iCub, showing the applicability of our approach
Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations
The ability to learn new tasks and quickly adapt to different variations or
dimensions is an important attribute in agile robotics. In our previous work,
we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm
policy representation to facilitate the learning and execution of assembly
tasks. The current implementation of the BTMGs for a specific task may not be
robust to the changes in the environment and may not generalize well to
different variations of tasks. We propose to extend the BTMG policy
representation with a module that predicts BTMG parameters for a new task
variation. To achieve this, we propose a model that combines a Gaussian process
and a weighted support vector machine classifier. This model predicts the
performance measure and the feasibility of the predicted policy with BTMG
parameters and task variations as inputs. Using the outputs of the model, we
then construct a surrogate reward function that is utilized within an optimizer
to maximize the performance of a task over BTMG parameters for a fixed task
variation. To demonstrate the effectiveness of our proposed approach, we
conducted experimental evaluations on push and obstacle avoidance tasks in
simulation and with a real KUKA iiwa robot. Furthermore, we compared the
performance of our approach with four baseline methods
Learning to Avoid Obstacles With Minimal Intervention Control
Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g., writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems: (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot
A constrained DMPs framework for robot skills learning and generalization from human demonstrations
Dynamical movement primitives (DMPs) model is a useful tool for efficiently robotic learning manipulation skills from human demonstrations and then generalizing these skills to fulfill new tasks. It is improved and applied for the cases with multiple constraints such as having obstacles or relative distance limitation for multi-agent formation. However, the improved DMPs should change additional terms according to the specified constraints of different tasks. In this paper, we will propose a novel DMPs framework facing the constrained conditions for robotic skills generalization. First, we conclude the common characteristics of previous modified DMPs with constraints and propose a general DMPs framework with various classified constraints. Inspired by barrier Lyapunov functions (BLFs), an additional acceleration term of the general model is deduced to compensate tracking errors between the real and desired trajectories with constraints. Furthermore, we prove convergence of the generated path and makes a discussion about advantages of the proposed method compared with existing literature. Finally, we instantiate the novel framework through three experiments: obstacle avoidance in the static and dynamic environment and human-like cooperative manipulation, to certify its effectiveness
Robots Learning Manipulation Tasks from Demonstrations and Practice
Developing personalized cognitive robots that help with everyday tasks is one of the on-going topics in robotics research. Such robots should have the capability to learn skills and perform tasks in new situations. In this thesis, we study three research problems to explore the learning methods of robots in the setting of manipulation tasks. In the first problem, we investigate hand movement learning from human demonstrations. For practical purposes, we propose a system for learning hand actions from markerless demonstrations, which are captured using the Kinect sensor. The algorithm autonomously segments an example trajectory into multiple action units, each described by a movement primitive, and forms a task-specific model. With that, similar movements for different scenarios can be generated, and performed on Baxter Robots.
The second problem aims to address learning robot movement adaptation under various environmental constraints. A common approach is to adopt motion primitives to generate target motions from demonstrations. However, their generalization capability is weak for novel environments. Additionally, traditional motion generation methods do not consider versatile constraints from different users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt the movement of robot end-effectors for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with different constraints. The framework also considers user feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. Experiments on a humanoid platform validate the effectiveness of our approach.
In order to further adapt robots to perform more complex manipulation tasks, as the third problem, we are investigating a framework that the robot could not only plan and execute the sequential task in a new environment, but also refine its actions by learning subgoals through re-planning/re-execution during the practice. A sequential task is naturally considered as a sequence of pre-learned action primitives, each action primitive has its own goal parameters corresponding to the subgoal. We propose a system to learn the subgoals distribution of given task model using reinforcement learning by iteratively updating the parameters in the trials. As a result, by considering the learned subgoals distribution in sequential motion planning, the proposed framework could adaptively select better subgoals to generate movements for robot to execute the task successfully. We implement the framework for the task of ''openning a microwave'' involving a sequence of primitive actions and subgoals and validate it on Baxter platform
Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces
QueiĂer J, Steil JJ. Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces. Frontiers in Robotics and AI. 2018;5:49.Modern robotic applications create high demands on adaptation of actions with respect to
variance in a given task. Reinforcement learning is able to optimize for these changing conditions,
but relearning from scratch is hardly feasible due to the high number of required rollouts. We
propose a parameterized skill that generalizes to new actions for changing task parameters,
which is encoded as a meta-learner that provides parameters for task-specific dynamic motion
primitives. Our work shows that utilizing parameterized skills for initialization of the optimization
process leads to a more effective incremental task learning. In addition, we introduce a hybrid
optimization method that combines a fast coarse optimization on a manifold of policy parameters
with a fine grained parameter search in the unrestricted space of actions. The proposed algorithm
reduces the number of required rollouts for adaptation to new task conditions. Application in
illustrative toy scenarios, for a 10-DOF planar arm, and a humanoid robot point reaching task
validate the approach
Computational Methods for Cognitive and Cooperative Robotics
In the last decades design methods in control engineering made substantial progress in
the areas of robotics and computer animation. Nowadays these methods incorporate the
newest developments in machine learning and artificial intelligence. But the problems
of flexible and online-adaptive combinations of motor behaviors remain challenging for
human-like animations and for humanoid robotics. In this context, biologically-motivated
methods for the analysis and re-synthesis of human motor programs provide new insights
in and models for the anticipatory motion synthesis.
This thesis presents the authorâs achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter âGoal-directed Imitation for Robotsâ considers imitation learning in cognitive and developmental robotics.
The work presented here details the authorâs progress in the development of hierarchical
motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of âlearning for
imitationâ and âlearning by imitationâ. The complete system includes a low-level real-time
capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts.
Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the
problems of task-related action transitions are considered in the second part of the thesis
âKinematic Motion Synthesis for Computer Graphics and Roboticsâ. In this part, a new
approach of modeling complex full-body human actions by mixtures of time-shift invariant
motor primitives in presented. The online-capable full-body motion generation architecture
based on dynamic movement primitives driving the time-shift invariant motor synergies
was implemented as an online-reactive adaptive motion synthesis for computer graphics
and robotics applications.
The last chapter of the thesis entitled âContraction Theory and Self-organized Scenarios
in Computer Graphics and Roboticsâ is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last
part presents new mathematical tools for stability analysis and synthesis of multi-agent
cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung
komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen
Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage
neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der
kĂŒnstlichen Intelligenz. Die flexible und echtzeitfĂ€hige Kombination von motorischen Verhaltensweisen
ist eine wesentliche Herausforderung fĂŒr die Generierung menschenĂ€hnlicher
Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch
motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme
neue Erkenntnisse und Modelle fĂŒr die antizipatorische Bewegungssynthese.
Diese Dissertation prÀsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der
kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie
intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der
Dissertation im Kapitel âZielgerichtete Nachahmung fĂŒr Roboterâ behandelt das Imitationslernen
in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben
neue Methoden fĂŒr die hierarchische Bewegungserkennung und -planung, die durch
Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert
wurden. Die entwickelte Architektur ist in der Lage, âdurch Imitation zu lernenâ
und âzu lernen zu imitierenâ. Das komplette entwickelte System enthĂ€lt ein echtzeitfĂ€higes
Pfadplanungssubsystem zur Hindernisvermeidung wĂ€hrend der DurchfĂŒhrung von Armbewegungen.
Das lernbasierte Pfadplanungssubsystem ist universell und fĂŒr alle Arten von
anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer
Handlungen zu ĂŒbertragen.
Im zweiten Teil der Arbeit âKinematische Bewegungssynthese fĂŒr Computergrafik und
Robotikâ werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h.
von rÀumlichen und rÀumlich-zeitlichen Kombinationen motorischer Bewegungselemente
bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs ĂŒbergĂ€ngen behandelt.
Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen
durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem
wurde ein online-fĂ€higer Synthesealgorithmus fĂŒr Ganzköperbewegungen entwickelt, der
auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten
verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde fĂŒr
verschiedene Probleme der Bewegungssynthese fĂŒr die Computergrafik- und Roboteranwendungen
implementiert.
Das letzte Kapitel der Dissertation mit dem Titel âKontraktionstheorie und selbstorganisierte
Szenarien in der Computergrafik und Robotikâ widmet sich optimalen Kontrollstrategien
in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare
Kinematik gekennzeichnet sind. Dieser letzte Teil prÀsentiert neue mathematische Werkzeuge
fĂŒr die StabilitĂ€tsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien
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